AI-generated code security
What is AI-generated code security?
The distinct security risks that come with AI-written code. Models reproduce insecure patterns from their training data, and developers tend to trust the output more than they should. A large share of AI-generated code samples carry a common vulnerability. The answer is not to stop, but to scan, review and test AI output as rigorously as any other code.
Why it matters
The individual risk is well known; the organisational one is arithmetic. When a team ships two or three times as much code per sprint, the rate of insecure patterns need not rise for the absolute number of vulnerabilities to climb sharply, because there is simply far more code carrying them. More surface, more to review, more that can go wrong in production. On top of that sits a provenance question the speed obscures. Generated code can reproduce snippets from training data, dragging in licensing uncertainty and known-bad patterns with it. None of this is an argument to stop. It is an argument to scale the guardrails with the output, so scanning, dependency checks and human review grow at the same pace as the code, instead of staying sized for the volume a team used to write by hand.
In practice
A team celebrates shipping far more per sprint, and its security scanning, unchanged, keeps flagging the same small percentage of issues. The percentage is steady; the absolute count is not, because the denominator doubled. The fix is not complicated: make security review scale with throughput, not with headcount, or the extra speed just ships extra risk.
